R Command-Line Program

RStudio is good for writing and testing your R code, but for work that needs repetitions or takes a long time to finish, it may be easier to run your program/script in command line instead.

Before we start, open the RStudio project you created following the RStudio project organization recomendations in the Overview section (assuming you downloaded and saved the bike counts data to the data directory of the project).

We can create an R script (from the File/New File/R Script menu of RStudio) that load the bike counts for Hawthorne Bridge:

library(tidyverse)

input_file <- "data/Hawthorne Tilikum Steel daily bike counts 073118.xlsx"
bridge_name <- "Hawthorne"
bikecounts <- read_excel(input_file,        #path - the path to the input excel file
                         sheet=bridge_name, #name/number of the sheet, it uses name of the bridge
                         skip=1)            #since each worksheet has a two-row header, skip the first row
#names(bikecounts) <- c("date", "westbound", "eastbound", "total")
bikecounts$bridge <- bridge_name

head(bikecounts)

Choose a file name, for example, load_data.R, and save the script in the code directory of your RStudio project (created a code directory first if you haven’t yet).

Now we can run the script in a command line shell (you can open one in RStudio’s Tools/Shell… menu):

Rscript code/load_data.R

Notice that the script may not print out outputs on the screen when called in the command line unless you explicitly call the print function.

But what if we have many files for which we would like to repeatedly show the basic information (rows, data types etc)? We can refactor our script to accept the file name and bridge name from command line arguments, so that the script can work with any acceptable files.

In an R script, you can use commandArgs function to get the command line arguments:

args <- commandArgs()
print(args)

So in our case, our script should take input_file and bridge_name from the command line arguments, we can get the value of the arguments with:

args <- commandArgs()
input_file <- args[1]
bridge_name <- args[2]

Replace the two lines in load_data.R starting with input_file and bridge_name with these three lines.

Now our script can be invoked in the command line with:

Rscript code/load_data.R "data/Hawthorne Tilikum Steel daily bike counts 073118.xlsx" \
Hawthorne

(The quotation marks are needed for the file name when there are spaces in the name and “" breaks a command into two lines.)

(Optional) Debugging with RStudio

This section is adapted from Visual Debugging with RStudio.

1. Download foo.R from https://raw.githubusercontent.com/cities/datascience2017/master/code/foo.R and save it to the code dirctory of your project folder;

2. Open foo.R and source it;

3. In the RStudio Console pane of type foo("-1") and then enter.

Why does the foo function claim “-1 is larger than 0”? Let’s debug the foo function and find out.

My code example

Download the file

library(readxl)
library(lubridate)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

input_file <- "data/Hawthorne Tilikum Steel daily bike counts 073118.xlsx"
bridge_name <- "Hawthorne"

# define a funtion that load bike counts data
load_data <- function(input_file, bridge_name) {
  bikecounts <- read_excel(input_file,
                           sheet = bridge_name,
                           skip = 1)
  bikecounts$name <- bridge_name
  bikecounts
}

Tilikum <- load_data(input_file, "Tilikum")
Hawthorne <- load_data(input_file, "Hawthorne")

# use the column names of Tilikum for Hawthorne
names(Hawthorne) <- names(Tilikum)

Steel <- load_data(input_file, "Steel")
names(Steel) <- c("date", "lower", "westbound", "eastbound", "total", "name")

# combine all three data frame for all three bridges
bikecounts <- bind_rows(Hawthorne, 
                        Tilikum, 
                        Steel %>% select(-lower)) # exclude the `lower` col in Steel data frame

# average daily bike counts by bridge
bikecounts %>% 
  group_by(name) %>% 
  summarize(avg_daily_counts=mean(total, na.rm=TRUE))

## # A tibble: 3 x 2
##   name      avg_daily_counts
##   <chr>                <dbl>
## 1 Hawthorne            3904.
## 2 Steel                2240.
## 3 Tilikum              1725.

# average monthly bike counts by bridge
bikecounts %>% 
  # first create ym column as a unique month identifier
  group_by(name, ym=floor_date(date, "month")) %>%
  summarize(total_monthly_counts=sum(total), counts=n()) %>% 
  # then average by month over years for each bridge
  group_by(name, month(ym)) %>% 
  summarize(avg_monthly_counts=mean(total_monthly_counts))

## # A tibble: 36 x 3
## # Groups:   name [?]
##    name      `month(ym)` avg_monthly_counts
##    <chr>           <dbl>              <dbl>
##  1 Hawthorne           1             79782.
##  2 Hawthorne           2             81534.
##  3 Hawthorne           3            100500.
##  4 Hawthorne           4            130006.
##  5 Hawthorne           5            155227.
##  6 Hawthorne           6            151171.
##  7 Hawthorne           7            138820.
##  8 Hawthorne           8            164198.
##  9 Hawthorne           9            142954.
## 10 Hawthorne          10            126626 
## # ... with 26 more rows